1. Field of the Invention
The present invention relates to an electroencephalograph (EEG) signal analysis system which determines and displays, on a real-time basis, the frequency content of EEG signals from the brain.
2. Description of the Prior Art
An electroencephalograh (EEG) is a device which measures and records brain wave activity by sensing electrical potential of a patient's scalp, cortex or cerebrum at various sites. Each EEG channel corresponds to a particular electrode combination attached to the patient. The sensed EEG potential at each channel is amplified by a differential amplifier, and the amplifier output signal is typically used to control movement of a recording pen of a polygraph. The EEG record is a long strip of polygraph paper containing a waveform for each EEG channel. The polygraph paper is driven at a predetermined rate (e.g. 30 millimeters per second) and is graduated to represent predetermined time increments. A neurologist must evaluate the EEG record to determine abnormalities in the EEG waveforms.
EEG signals exhibit different frequencies depending upon brain activity. The EEG signal frequencies are classified into four basic frequency bands, which are generally referred to as "delta" (0 to 3.5 Hertz); "theta" (4 to less than 8 Hertz); "alpha" (8 to 13 Hertz); and "beta" (greater than 13 Hertz). The neurologist determines the predominant frequency of a particular channel during a particular time period by measuring the period of the EEG signal waveform shown on the EEG record. This requires considerable training and is highly dependent upon the skill of the neurologist, since the EEG signal waveform typically includes multiple frequency components.
In general, electronic equipment developed in the past for EEG analysis has been designed primarily for the acquisition of data, with little emphasis on the analysis of that data. Although computers were introduced into EEG technology in the early 1970's, there has been limited acceptance of computer-assisted EEG analysis due to a limited number of channels which are analyzed and a lack of an intuitive display. Existing computerized EEG technology has required a high degree of specialized knowledge to understand the information being displayed and, as a result, the market for that technology has been limited to a relatively small number of specialists in the field of electroencephalography.
One type of EEG signal analysis which has been performed by computers in the past has been called a "spectral analysis" or "compressed spectral array". In this type of analysis, the analog EEG signal for each channel is periodically sampled, converted to a digital value and stored. The stored digital data represents an EEG signal waveform (i.e. the amplitude of the EEG signal as a function of time). The computer converts the stored digital data from the time domain to the frequency domain by means of a Fast Fourier Transform (FFT) algorithm. The transformed data represents a frequency spectrum (i.e. amplitude or power of the EEG signal as a function of frequency). The computer provides a printout or display which is formed by a series of staggered two-axis graphs of amplitude versus frequency representing the frequency spectra from different time periods or epochs. The resulting display looks somewhat similar to a mountain range.
Compressed spectral array analysis has a number of significant disadvantages. First, it uses an extremely complex and nonintuitive form of display, which requires a great deal of skill to interpret. Second, the display can only be interpreted at a very close range, even by skilled personnel. Third, compressed spectral array analysis is not performed on a real-time basis in more than four channels.
There are a number of important applications of EEG analysis which have not been possible or have been extremely inconvenient with the prior art EEG systems. One important application is in the monitoring of cerebral functions within an operating room during a surgical procedure such as a carotid endarterectomy.
About forty percent of the blood flow to the human brain is provided by each of the two carotid arteries. These arteries can become hardened, thus limiting blood flow to the brain, and in that case a carotid endarterectomy is necessary to strip the carotid arteries.
One of the critical decisions during a carotid endarterectomy is whether the patient will need a shunt during the time when the artery is clamped and is being stripped. Some patients have sufficient blood flow to the brain from other areas so that the clamping of the carotid artery does not endanger the patient's brain due to insufficient blood flow. In a substantial number of cases, however, the blood flow is insufficient, and the clamping of the artery can cause the patient to have a stroke unless a shunt is provided. Unfortunately, it is almost never possible to know beforehand whether a shunt will be necessary. The problem with using a shunt is that it increases the chance of the patient having a stroke by about ten percent, because blood clots tend to adhere to the shunt, clots break off from the unit, or the inside of the vessel may tear and block the artery.
The most sensitive technique for determining whether a shunt will be needed during a carotid endarterectomy involves monitoring cerebral activity by means of EEG equipment. EEG monitoring has not, however, found wide use because it is very tedious and requires the presence of a trained neurologist to interpret the EEG record which is produced. Having a neurologist as well as EEG technicians in the operating room for long periods of time simply for the purpose of EEG monitoring significantly increases the total cost of the surgery. There is a need for an EEG signal analyzer which will automatically analyze EEG signals on a real-time basis during surgery, and which will provide an intuitive output which can be easily understood by the surgeon without the need for the presence of a neurologist during surgery and which provides an audible warning.
Other areas in which improvements in EEG technology would be desirable include the monitoring of patients or subjects during altered states of awareness or during psychological experimentation or assessment, monitoring in intensive care units and recovery rooms, and automatic interpretation of routine outpatient EEG. At the present time, monitoring using EEG typically involves the use of FM tape recorders which record the EEG signals for subsequent off-line analysis. This is a very costly procedure, in that the cost of the recording equipment increases significantly with each additional channel to be recorded. There is no on-line (or real-time) dynamic monitoring possible, since the EEG signals are being recorded for subsequent analysis. The lack of on-line or real-time updates in the prior art significantly limits the types of monitoring which can be performed and the utility of those types of monitoring which are possible.